census 2011 - statistical release [p030142011]
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T HE S OU TH A FR IC A I K NO W, T HE H OM E I U ND ER ST AN D
Statistical release
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De Bruyn Park Bui l di ng, 170 Thabo Sehume Street, Pretori a, 0002
Pri v ate Bag X44, Pretori a, 0001, South Afri ca
U s e r i n f or m a t io n s e r v ic e : + 2 7( 1 2 ) 3 10 8 6 0 0 , Fa x : + 2 7( 1 2 ) 3 10 8 5 0 0
M a i n s w it c h b o ar d : + 2 7 ( 12 ) 3 1 0 8 9 1 1 , Fa x : + 2 7 ( 12 ) 3 2 1 7 3 8 1
Website: www.statssa.gov.za, Email: info@statssa.gov.za
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Statistical releaseP0301.4
Census 2011
Embargoed until:30 October 2012
10:00
Enquiries:
Angela NgyendeTel. (012) 310 4699
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Census 2011 Statistical release P0301.4 / Statistics South Africa
Published by Statistics South Africa, Private Bag X44, Pretoria 0001
Statistics South Africa, 2012Users may apply or process this data, provided Statistics South Africa (Stats SA) is acknowledged as the originalsource of the data; that it is specified that the application and/or analysis is the result of the user's independentprocessing of the data; and that neither the basic data nor any reprocessed version or application thereof may besold or offered for sale in any form whatsoever without prior permission from Stats SA.
Stats SA Library Cataloguing-in-Publication (CIP) DataCensus 2011 Statistical release P0301.4 / Statistics South Africa. Pretoria: Statistics South Africa, 2012
78 pp
A complete set of Stats SA publications is available at Stats SA Library and the following libraries:
National Library of South Africa, Pretoria DivisionNational Library of South Africa, Cape Town DivisionLibrary of Parliament, Cape TownBloemfontein Public LibraryNatal Society Library, PietermaritzburgJohannesburg Public LibraryEastern Cape Library Services, King Williams TownCentral Regional Library, PolokwaneCentral Reference Library, NelspruitCentral Reference Collection, KimberleyCentral Reference Library, Mmabatho
This publication is available on the Stats SA website: www.statssa.gov.za
For technical enquiries please contact:
Calvin MolongoanaTel: 012 310 4754Fax: 012 310 4865Email: calvinm@statssa.gov.za
For dissemination enquiries please contact Printing and Distribution, Statistics South Africa
Ina du PlessisEmail: inadp@statssa.gov.za
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Cautionary notes
Data
- Comparison of Census 2011 and previous Censuses requires alignment of data to 2011 municipal
boundaries
Disability
- Questions on disability were replaced by General health and functioning questions. Due to change in
question, 2011 results are not comparable with previous Censuses 1996 and 2001.
- Due to misreporting on general health and functioning questions for children younger than five years, data
on this variable are only profiled for persons five years and older.
Labour statistics (employment)
- Quarterly Labour Force Survey (QLFS) remains the official source of labour statistics
- The QLFS_Q4: 2011 has not been benchmarked to Census 2011 figures and the differences between thetwo are therefore only broadly indicative
- Boundaries - provincial trends over time are difficult to establish due to changes in boundaries
o Census is a de facto measure of the population; while QLFS survey is a de jure measure.
o The reference period in Census (Census night) is fixed while it is a moving reference period over
three months for QLFS
- Formal and informal sector:
o An objective measure is used in the QLFS based on vat/income tax registration and establishment
size, a subjective measure is used in Census 2011. Also, in line with ILO guidelines, persons
employed in agriculture and private households are not usually included in the formal and informal
sectors, but are identified as separate categories. It is not currently possible to identify agricultural
employment in Census 2011, since the coding of industry and occupation has not yet been
completed. Sectoral distributions therefore include persons employed in agriculture. And with
regards to persons employed in private households, the results are not based on the relevant
questions that determine the international classification for industry but instead are based on the
question which determines the sector in which respondents were employed. Thus, after coding is
completed the numbers may change.
Rounding off
Due to rounding, the displayed totals in the tables do not always match the sum of the displayed rows or columns.
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Contents
Contents ....................................................................................................................................................................... iii
1. INTRODUCTION ..................................................................................................................................................1
1.1 Overview .......................................................................................................................................................1
1.2 How the count was done ..............................................................................................................................1
1.2.1 Planning ................................................................................................................................................1
1.2.2 Pre-enumeration ...................................................................................................................................1
1.2.3 Enumeration ..........................................................................................................................................1
1.2.4 Data processing ....................................................................................................................................2
1.2.5 Data editing and validation system .......................................................................................................2
1.2.5.1 Editing team ..................................................................................................................................2
1.2.5.2 Role of the team ............................................................................................................................2
1.2.5.3 Editing strategy for Census 2011 ..................................................................................................3
1.2.6 Independent monitoring and evaluation of Census field activities ........................................................3
1.2.7 Post-enumeration survey (PES) ...........................................................................................................3
1.2.7.1 Preparations for the PES .....................................................................................................................4
1.2.7.2 Methodology .................................................................................................................................4
1.2.7.3 Sampling .......................................................................................................................................5
1.2.7.4 Questionnaire development ..........................................................................................................5
1.2.7.5 Fieldwork methodology .................................................................................................................5
1.2.7.6 Matching and reconciliation methodology .....................................................................................5
1.2.7.7 PES data collection .......................................................................................................................6
1.2.7.8 Matching and reconciliation ..........................................................................................................6
1.2.7.9 Estimation and tabulation..............................................................................................................7
1.2.8 Conclusion ................................................................................................................................................8
2. GEOGRAPHY OF SOUTH AFRICA ....................................................................................................................9
2.1 Provincial boundary changes: 2001 to 2011 ................................................................................................9
2.2 Local municipal boundary changes, 20012011 ........................................................................................11
2.3 Comparing Census 2011 with previous Censuses .....................................................................................13
3. FINDINGS ...........................................................................................................................................................14
3.1 Demographic characteristics ......................................................................................................................14
3.1.1 Introduction .........................................................................................................................................14
3.1.2 Population size ....................................................................................................................................14
3.2 Population composition ...............................................................................................................................17
3.3 Sex ratio ......................................................................................................................................................17
3.4 Median age .................................................................................................................................................20
3.5 Population structure ....................................................................................................................................21
3.6 Concluding remarks ....................................................................................................................................22
3.7 Migration .....................................................................................................................................................22
3.7.1 Introduction .........................................................................................................................................22
3.7.2 Patterns of migration between Censuses 2001 and 2011 .................................................................. 22
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3.7.3 Life-time migration patterns ................................................................................................................24
3.8 Citizenship ..................................................................................................................................................26
3.9 Education ....................................................................................................................................................26
3.9.1 Introduction .........................................................................................................................................26
3.9.2 Attendance at an educational institution .............................................................................................27
3.10 Average annual household income ............................................................................................................36
3.11 General health and functioning ...................................................................................................................38
3.11.1 Introduction .........................................................................................................................................38
3.12 Labour market status ..................................................................................................................................41
3.12.1 The labour market in South Africa ......................................................................................................41
3.12.1.1 Introduction ................................................................................................................................41
3.12.1.2 National labour market results from Census 2011 and the QLFS_Q4:2011 .............................. 42
3.12.1.3 Provincial labour market rates based on Census 2011 and QLFS_Q4:2011 .............................43
3.12.1.4 Key labour market rates by population group, based on Census 2011 ...................................... 44
3.12.1.5 Key labour market rates by sex and population group, based on Census 2011 ........................ 45
3.12.1.6 Key labour market rates by age group based on Census 2011 ................................................. 47
3.13 Conclusion ..................................................................................................................................................48
3.14 Housing .......................................................................................................................................................49
3.14.1 Introduction .........................................................................................................................................49
3.15 Conclusion ..................................................................................................................................................59
3.16 Mortality data: Household deaths ...............................................................................................................59
3.17 Parental survival .........................................................................................................................................68
List of maps
Map 2.1: Provincial boundary changes since 2001 .................................................................................................... 10Map 2.2: Municipal boundary changes since 2001 .................................................................................................... 12
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1. INTRODUCTION
1.1 Overview
Censuses are principal means of collecting basic population and housing statistics required for social and
economic development, policy interventions, their implementation and evaluation. South Africa has conductedthree Censuses (1996, 2001 and 2011). Census 2011 was the third Census to be conducted since the post
democratic elections in 1994 and a number of population and household attributes were measured and variety of
indicators generated. This chapter provides profiles results on all Census topics; demographics, migration,
education, general health and functioning, labour force, mortality, and households.
1.2 How the count was done
Census 2011 was conducted from 9th
to 31st
October 2011. This section focuses on the various activities that were
carried out prior to the finalisation of the results. They can be summarised as follows: Planning, Pre-enumeration,
Enumeration, Processing and Editing.
1.2.1 Planning
This process involved the development of the overall strategy, the structure for the project, component plans and
budget. These processes were started in 2003 and were subsequently reviewed in 2008, after the completion of
the Community Survey (CS) in 2007. Methodologies and procedures were then developed and tested in a form of
mini tests and pilot in 2008 and 2009 respectively. The findings from these tests helped to refine the plans and
methods for the final test in 2010 called the Dress Rehearsal. The latter was expected to be a replica of how the
actual count was to be conducted in 2011, and therefore the timing had to be the same month as the main Census,
i.e. October month.
1.2.2 Pre-enumeration
The pre-enumeration phase mainly involved the final preparatory work before the actual count. It started with massproduction of Census instruments like questionnaires, manuals, field gear etc. The phase also involved acquisition
of satellite offices required in the districts, recruitment of the 1st
level of field management staff (District Census
Coordinators - 130 DCCs) and Field work Co-ordinators 6 000 FWCs. These groups of people were then given
intense training based on their key performance areas. At the same time the country was being sub-divided into
small pockets called enumeration areas (EAs); the underlying principle for this sub-division is that an EA should be
within reach of a Fieldworker and all households in that EA can be covered within the allocated number of days.
This process yielded 103 576 EAs. The other benefit for this sub-division is the finalisation of the distribution plan of
all materials required in the provinces and districts. It also gives a better estimate of the number of field staff to
recruit for the count. The pre-enumeration phase involved over 7 000 staff.
1.2.3 Enumeration
The enumeration phase, started with the training of supervisors as listers. Each person had to list all dwellings
within an EA and had a minimum of 4 EAs to cover. These areas were called supervisory units. As they were
listing, they were also expected to publicise the activities of the Census within their supervisory units. Upon
completion of listing, final adjustments of workload and number of enumerators required were finalised. Training of
enumerators started in earnest, and it mainly covered how to complete the questionnaire and to read a map. The
latter was to aid them to identify the boundaries of their assigned areas. An enumerator was also given a few days
before the start of the count to update their orientation book with any developments that might have happened
since listing, as well as introduce themselves to the communities they were to work with, through posters bearing
their photos and special identification cards. On the night of the 9th
October the actual count started with the
homeless and special institutions given special attention. The enumeration phase was undertaken by an army of
field staff in excess of 160 000, inclusive of management.
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1.2.4 Data processing
The processing of over 15 million questionnaires commenced in January 2012, immediately after the completion of
the reverse logistics in December 2011. Each box and its contents were assigned a store location in the processing
centre via a store management system. Each time a box was required for any process it was called through this
system. The processing phase was sub-divided in the following processes: primary preparation - where all
completed questionnaires were grouped into clusters of 25 and the spine of the questionnaire cut off. Secondarypreparation - where questionnaires were finally prepared for scanning, by removing foreign materials in between
pages and ensure that all pages are loose. Scanning - questionnaires were put through a scanner to create an
electronic image. Finally Tilling and completion - where any unrecognised reading/ badly-read image by the
scanner had to be verified by a data capturer. This process took 8 months. Over 2 000 data processors working 3
shifts per day were employed for this phase to ensure that 225 million single pages are accounted for.
1.2.5 Data editing and validation system
The execution of each phase of Census operations introduces some form of errors in Census data. Despite quality
assurance methodologies embedded in all the phases; data collection, data capturing (both manual and
automated), coding, and editing, a number of errors creep in and distort the collected information. To promote
consistency and improve on data quality, editing is a paramount phase in identifying and minimising errors such as
invalid values, inconsistent entries or unknown/missing values. The editing process for Census 2011 was based on
defined rules (specifications).
The editing of Census 2011 data involved a number of sequential processes: selection of members of the editing
team, review of Census 2001 and 2007 Community Survey editing specifications, development of editing
specifications for the Census 2011 pre-tests (2009 pilot and 2010 Dress Rehearsal), development of firewall editing
specifications and finalisation of specifications for the main Census.
1.2.5.1 Editing team
The Census 2011 editing team was drawn from various divisions of the organisation based on skills and
experience in data editing. The team thus composed of subject matter specialists (demographers and
programmers), managers as well as data processors.
Census 2011 editing team was drawn from various divisions of the organization based on skills and experience in
data editing. The team thus composed of subject matter specialists (demographers and programmers), managers
as well as data processors
1.2.5.2 Role of the team
Among other Census activities, editing team roles and responsibilities included:
x Establishment of editing plan/schedule
x Formulation and application of clear and concise editing specifications
x Validation of Census data using other data sources
x Ensuring of consistency of editing rules between Censuses (2001 and 2011) where applicable
x Provision of imputation flags and rates
x Identification of errors and provide corrections where possible
x Review and refinement of the edit specifications based on edit trail evaluations, cross tabulations, and
comparison of Census data with other datasets
x Testing the specifications before confirming and applying them
Editing specification process commenced with activities relating to review of existing editing specifications
guidelines. Census 2001 specifications as well as Community Survey 2007 survey specifications and UN handbook
on Census editing were reviewed to form the basis of the specifications.
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1.2.5.3 Editing strategy for Census 2011
The Census 2011 questionnaire was very complex, characterised by many sections, interlinked questions and
skipping instructions. Editing of such complex, interlinked data items required application of a combination of
editing techniques. Errors relating to structure were resolved using structural query language (SQL) in Oracle
dataset. CSPro software was used to resolve content related errors. The strategy used for Census 2011 data
editing was implementation of automated error detection and correction with minimal changes. Combinations oflogical and dynamic imputation were used. Logical imputations were preferred, and in many cases substantial effort
was undertaken to deduce a consistent value based on the rest of the households information. To profile the
extent of changes in the dataset and assess the effects of imputation, a set of imputation flags are included in the
edited dataset. Imputation flags values include the following:
Census 2011 questionnaire was very complex, characterized by many sections, interlinked questions and skipping
instructions. Editing of such complex, interlinked data items required application of a combination of editing
techniques. Errors relating to structure were resolved using structural query language (SQL) in Oracle dataset.
CSPro software was used to resolve content related errors. The strategy for Census 2011 data editing was
implementation of automated error detection and correction with minimal changes. Combinations of logical and
dynamic imputation were used. Logical imputations were preferred, and in many cases substantial effort was
undertaken to deduce a consistent value based on the rest of the households information. To profile the extent of
changes in the dataset and assess the effects of imputation, a set of imputation flags are included in the edited
dataset. Imputation flags values include the following:
0 no imputation was performed; raw data were preserved
1 Logical editing was performed, raw data were blank
2 logical editing was performed, raw data were not blank
3 hot-deck imputation was performed, raw data were blank
4 hot-deck imputation was performed, raw data were not blank
1.2.6 Independent monitoring and evaluation of Census field activities
Independent monitoring of the Census 2011 field activities was carried out by a team of 31 professionals and 381
Monitoring and Evaluation Monitors from Monitoring and Evaluation division. These included field training, publicity,
listing and enumeration. This was to make sure that the activities were implemented according to the plans and
have independent reports on the same. They also conducted Census 2011 and the Post Enumeration Survey
(PES) Verification studies to identify the out-of-scope cases within Census (a sample of 7 220 EAs) and the PES
sample (600 EAs) as reported in the Census 2011 PES EA Summary Books.
1.2.7 Post-enumeration survey (PES)
A post-enumeration survey (PES) is an independent sample survey that is conducted immediately after the
completion of Census enumeration in order to evaluate the coverage and content errors of the Census. The PES
for Census 2011 was undertaken shortly after the completion of Census enumeration, from November to
December 2011, in approximately 600 enumeration areas (EAs) (which later increased to 608 due to subdivision of
large EAs). The main goal of the PES was to collect high quality data that would be compared with Census data in
order to determine how many people were missed in the Census and how many were counted more than once.
A population Census is a massive exercise, and while every effort is made to collect information on all individuals in
the country, including the implementation of quality assurance measures, it is inevitable that some people will be
missed and some will be counted more than once. A PES assists in identifying the following types of errors:
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x Coverage error: this includes both erroneous omissions (e.g. a household that was not enumerated) and
erroneous inclusions (e.g. a household that moved into the enumeration area (EA) after Census but was still
enumerated, or a household that was enumerated more than once).
x Content error: this refers to the errors on the reported characteristics of the people or households
enumerated during Census.
The errors may emanate from the following reasons:
x Failure to account for all inhabited areas in the EA frame;
x EA boundary problems;
x Incomplete listing of structures and failure to identify all dwellings within an EA;
x Failure to enumerate/visit all listed dwellings within an EA;
x Failure to identify all households within a dwelling unit in instances whereby a dwelling unit has more than
one household;
x Failure to enumerate households (complete questionnaires) for all households due to refusals, unreturned
questionnaires for self-enumeration, inability to contact households, etc);
x Failure to include all individuals within households;
x Failure to observe the inclusion rule based on a persons presence on Census night (i.e. failure to apply thede facto rule accurately); and
x Lost questionnaires or damaged questionnaires that could not be processed.
Usually more people are missed during a Census, so the Census count of the population is lower than the true
population. This difference is called net undercount. Rates of net undercount can vary significantly for different
population groups depending on factors such as sex, age and geographic location. Stats SA obtains estimates of
the net undercount, including the type and extent of content errors (reported characteristics of persons and
households enumerated in the Census) using information collected through the PES.
1.2.7.1 Preparations for the PES
Planning involved the development of documents outlining the goal and objectives of the PES, timelines of theproject, identification of resources (financial, human and otherwise) required for implementing the project, and the
development of methodology documents. Timelines for the PES were synchronised with those of Census to ensure
the relevance of the project, and adhered to international best practice for maintaining a closed population between
Census and PES data collection, i.e. it should be carried out within a few months, preferably within six (6) months,
after the completion of Census fieldwork to ensure that the impact of natural population changes, such as births,
deaths and migration, as well as lapses in respondent recall do not complicate the exercise. Activities of the PES
included the following:
x Sampling: sample design and selection;
x Development of data collection methodologies: methods and procedures for data collection (publicity, listing
and enumeration), including quality control measures applied during data collection;x Development of matching and reconciliation procedures and systems: guidelines for matching, including
rules for determining the match status of households and individuals, as well as computer-based system for
capturing household and person records for matching purposes;
x Questionnaire development: selection of data items which allowed measurement of coverage and content,
including layout design and printing of questionnaire;
x Data collection: publicity, listing and enumeration of households in selected enumeration areas (EAs);
x Matching and reconciliation: office matching (comparison) of Census and PES household and person
records, and revisits to households in order to confirm or get more information that might assist in matching
unresolved cases; and
x Analysis and reporting: compilation of tables and report on PES results.
1.2.7.2 Methodology
The PES is an independent survey that replicates the Census in sampled enumeration areas (EAs). The major
assumption used in the PES is that the Census and the PES are independent, the estimate of the percentage
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missed by the PES but found by the Census, and the percentage missed by the Census but found by the PES, can
be used to construct estimates of the percentage missed by both PES and Census. The PES sought to estimate
the total number of persons and households in housing units on the night of 0910 October 2011 (Census night).
The units of observation were the persons who spent the Census night and/or the PES night in these living
quarters.
1.2.7.3 Sampling
The sampling frame for the PES was the complete list of Census 2011 EAs, amounting to 103 576 EAs. The
primary sampling units (PSUs) were the Census EAs. The principle for selecting the PES sample is that the EA
boundaries for sampled EAs should have well defined boundaries, and these boundaries should correspond with
those of Census EAs to allow for item-by-item comparison between the Census and PES records. The stratification
and sampling process followed will allow for the provision of estimates at national, provincial, urban (geography
type = urban) and non-urban (geography type = farm and traditional) levels, but estimates will only be reliable at
national and provincial levels. The sample of 600 EAs was selected and allocated to the provinces based on
expected standard errors which were based on those obtained in PES 2001. Populations in institutions (other than
Workers Hostels), floating and homeless individuals were excluded from the PES sample.
1.2.7.4 Questionnaire development
The approach to questionnaire design focused on capturing the main elements for measuring coverage and
content errors. Only a few elements from the Census 2011 questionnaire which were not likely to change within a
short period (that is between the Census and the PES reference nights) were retained. The questionnaire allowed
for the classification of each listed person as non-mover, in-mover, out-mover, or out-of-scope, with regard to
their household presence status on Census night (0910 October 2011). The data items for the PES questionnaire
included first name and surname, date of birth, age, sex, population group and presence of person in dwelling unit
on Census and/or PES night.
1.2.7.5 Fieldwork methodology
The PES replicated the Census in the sampled EAs, which meant that all methodologies and procedures for data
collection were based on Census methodologies and procedures. PES fieldwork was split into the following three
(3) phases; publicity and listing, enumeration and mop-up operations.
x Publicity and listing were conducted at the same time. Publicity focused on informing and educating
respondents and relevant stakeholders about the purpose of the PES to ensure successful coverage of all
dwelling units (DUs) in selected EAs. Listing involved the recording of all structures (including all DUs,
number of households in DUs and number of persons in households) in the sampled EAs in the EA
Summary Books.
x Enumeration involved interviewing respondents and recording responses in the fields provided in the PES
questionnaire. Self-enumeration for the PES was discouraged, but was used in instances where the
respondent insisted on self-enumeration.x Mop-up operations were conducted in the form of follow-up visits by senior field staff to households that
could not be contacted during the enumeration period.
1.2.7.6 Matching and reconciliation methodology
The matching exercise involved the comparison of household and person records in Census data and PES data. A
two-way case-by-case matching was conducted using the two sources: PES questionnaires and Census
questionnaires. Reconciliation visits were conducted in order to confirm or get more information that would assist in
matching unresolved cases, i.e. households or individuals enumerated in the Census that did not correspond with
households or individuals enumerated in the PES. Guidelines for matching, including rules for determining the
match status of households and individuals, were developed. A computer-assisted manual matching system was
developed for the capturing of data for matching purposes.
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1.2.7.7 PES data collection
PES data collection commenced immediately after the completion of Census fieldwork. The PES is a much smaller
scale operation (and hence easier to control) than the Census. These features enable the PES to deliver a more
accurate estimate of the percentage of people and dwellings missed by the Census. PES data collection (field
operations) was independent from Census operations and the following measures were taken to maintain the
operational independence of the PES:
x independent listing of enumeration areas (EAs) in the PES sample;
x using separate/independent office staff in the PES and Census where possible;
x ensuring the PES interviewers were not employed as Census field staff in the same area, and vice versa;
and
x maintaining the confidentiality of the PES sample so that Census field and office staff were not aware which
areas are included in the PES.
Temporary personnel (Fieldworkers and Fieldwork Supervisors) were recruited from the EAs/districts in which they
would be working and underwent rigorous training on fieldwork procedures to ensure that they deliver work of high
quality at the end of the fieldwork phase. Experienced permanent staff from Household Surveys (based in
provincial offices) was seconded to the project for the duration of data collection in supervisory positions to ensure
high quality data and minimise costs. The PES followed the integrated approach towards fieldwork; whereby 1
Fieldworker conducted publicity, listing and enumeration in 1 EA. A total of 768 Fieldworkers and Fieldwork
Supervisors were appointed for the collection of data in the 608 EAs (initially 600, but increased to 608 due to split
EAs). A ratio of 1 Fieldwork Supervisor for four (4) Fieldworkers was applied, but due to the spread of the sample in
various districts, this ratio could not always be applied.
1.2.7.8 Matching and reconciliation
The matching process involved the comparison of household and person records in Census data and PES data.
The main phases in the matching process were:
x Initial matching involved searching through the Census records in order to find the corresponding cases from
the PES enumeration records, and vice-versa (a two-way match);
x Capturing involved the capturing of PES and Census information on a capturing tool which formed part of the
computer-assisted manual matching system. Information for non-matched households and persons was also
captured;
x Computer-assisted matching which was the automated assigning of an initial match status for the household
and persons, and persons moving status. This process was done concurrently with the capturing process.
Classifications from initial matching are as follows:
1. Matched
2. possible match
In PES not in Census:
3. in PES not in Census - definite non-match
4. in PES not in Census - insufficient or unclear information
5. in-mover
6. born after Census
7. in Census not in PES;
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1. matched
In PES not in Census:
2. missed in Census3. PES erroneous inclusion - cases in PES not in Census that were outside
the EA boundaries or otherwise erroneously included in PES4. PES insufficient information - cases in PES not in Census for which a final
match status cannot be assigned due to insufficient information
5. in-mover6. born after Census
In Census not in PES:7. correctly enumerated in Census, missed in PES8. Census erroneous inclusion9. Census insufficient information cases in Census not in PES for which a
final match status cannot be assigned due to insufficient information
x Reconciliation visits are follow-up visits to households in the PES sampled EAs. The purpose of
reconciliation visits was to collect relevant information in order to determine the final match status of
unresolved cases identified during initial matching. Cases of possible match, in PES not in Census -
insufficient or unclear information, and in Census not in PES were considered unresolved and were sent to
the field for reconciliation; and
x Final matching involved the use of the results obtained from the reconciliation visits and initial matching
phases to assign a definite match status to each case. The table below illustrates the outcomes from final
matching.
1.2.7.9 Estimation and tabulation
Coverage measures were calculated only for cases belonging to the PES universe.
The initial estimates weighted estimates of total from the sample include the following:
a) Estimated number of non-movers;
b) Estimated number of out-movers;
c) Estimated number of matched non-movers;
d) Estimated number of matched out-movers;
e) Estimated number on in-movers;
f) Estimated number of erroneous inclusions in the Census; and
g) Estimated number of correctly enumerated persons missed in the PES
Dual system estimation was used to arrive at the true population of the country. This means that two independent
sources or systems are used to arrive at the estimate of the true population: the Census and the PES. Bothestimates contribute to the dual-system estimate, which is more complete than either the Census or the PES
estimate alone. In the end, this true population is compared with the Census-enumerated population and the
difference is the net undercount(orovercount). The following table indicates the undercount rates as estimated by
the PES.
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Net Census Coverage Error: Total and Rate by Province
ProvinceOmission rate for
personsOmission rate for
households
Western Cape 18,6 17,8
Eastern Cape 12,9 10,3
Northern Cape 13,4 14,8
Free State 10,1 9,4
KwaZulu-Natal 16,7 16,5
North West 14,9 17,0
Gauteng 14,7 15,2
Mpumalanga 15,5 14,4
Limpopo 10,0 9,6
All provinces 14,6 14,3
The adjustment procedure consisted of creating homogeneous adjustment classes with similar coverage rates and
calculating a common undercount rate, adjustment factor and adjustment figure for each class separately. The
adjusted figure for the total population was obtained by summing across the adjustment classes. In addition, only
the population of households received adjustment classes. The totals for the balance of the population, namely
people living in collective quarters and the homeless on the streets, were not adjusted.
1.2.8 Conclusion
The 2011 Census project had its own challenges and successes, like any other massive project. Be that as it may,
the following are worth mentioning; the Census fieldworkers who traverse the country to collect information from
households and those that we lost in the process. The respondents who opened their doors and locked their dogs
to aid the field staff to do their work, the processors who worked 24hrs/7days a week to ensure that the data can be
released within a year of enumeration. The Census management team who met daily for two years to steer the
project forward, the Stats SA EXCO for the leadership they provided, the Statistics Council and in particular the
sub-committee on population and social statistics for their continued guidance and support and finally the Minister
in the Presidency: responsible for planning for the robust interrogation of the plans and guidance on this project. It
is through such concerted efforts that as a country we can and will continuously improve on our endeavours.
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2. GEOGRAPHY OF SOUTH AFRICA
2.1 Provincial boundary changes: 2001 to 2011
A number of changes occurred in terms of provincial and municipal boundaries during the period between
Censuses 2001 and 2011. Of the nine provinces, only two provinces (Western Cape and Free State) were notaffected by changes. The provincial boundary changes were mostly as a result of eight cross boundary
municipalities which were absorbed in full into respective provinces.
Table 2.1: Geographical land area changes since 2001
Province name Provincial codeLand area in square
kilometres _2011Land area in square
kilometres 2001
Western Cape 1 129 462 129 449
Eastern Cape 2 168 966 169 954
Northern Cape 3 372 889 362 599
Free State 4 129 825 129 824
KwaZulu-Natal 5 94 361 92 305
North West 6 104 882 116 231
Gauteng 7 18 178 16 936
Mpumalanga 8 76 495 79 487
Limpopo 9 125 754 122 816
Total 1 220 813 1 219 602
The shift of the national boundary over the Indian Ocean in the North East corner of KwaZulu-Natal to cater for the Isimangaliso Wetland Parkled to the increase in South Africa's land area.
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Map 2.1: Provincial boundary changes since 2001
Provincial boundary changes mostly affected North West (land size decreased to 11348,9 square kilometres). Most
of this was absorbed by Northern Cape. The second largest decrease in land size was for Mpumalanga which
decreased by 2991,9 square kilometres with Limpopo being the main recipient of this land area.
It should be noted that the increased extent of KwaZulu-Natal is not mainly based on the exchange of Umzimkulu
(formerly in the Eastern Cape Province) and Matatiele (formerly in KwaZulu-Natal), but due to the shift of the
national boundary over the Indian Ocean in the north east corner of the province to cater for the iSimangaliso
Wetland Park. In terms of which areas moved to which province, a detailed outline is provided for below.
Northern Cape and North West:
x Ga Segonyana and Phokwane municipalities were cross boundary municipalities between Northern Cape
and North West in 2001 and were allocated to Northern Cape in full based on the current provincial
boundaries.
x Kagisano municipality (2001) was split into Kagisano/ Molopo municipality and Joe Morolong municipality,
with the former portion now in North West and the latter now part of the Northern Cape province.
x Moshaweng municipality (now part of Joe Morolong municipality) was incorporated in full in Northern Cape
based on the current provincial boundaries.
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North West and Gauteng
x Merafong City municipality (2001) was a cross boundary local municipality between North West and Gauteng
and was allocated to the Gauteng province based on the current provincial boundaries.
x West Rand (DMA) municipality (2001) was not aligned to the then provincial boundary and was absorbed
into Mogale City municipality in full based on the current provincial boundaries.
x City of Tshwane Metropolitan Municipality was a cross boundary municipality between Gauteng and NorthWest provinces. The portions adjacent to Moretele and Madibeng municipalities were allocated to Gauteng
in full based on the current provincial boundaries.
North West and Limpopo:
x Limpopo lost a portion of the Bela Bela municipality to North Wests Moretele municipality. In turn North West
lost a portion of the Moretele Municipality to Limpopos Bela Bela municipality based on the current provincial
boundaries.
Gauteng and Mpumalanga:
x A portion of Delmas municipality (2001) now called Victor Kanye was allocated to the City of Tshwane inGauteng based on the current provincial boundaries.
x Kungwini municipality, now incorporated into the City of Tshwane, was a cross boundary municipality and is
now fully allocated to Gauteng, based on the current provincial boundaries.
Mpumalanga and Limpopo:
x Greater Groblersdal, now Elias Motsoaledi, Greater Marble Hall now Ephraim Mogale, and Greater Thubatse
were cross boundary municipalities between Mpumalanga and Limpopo and have now been allocated in full
to the Limpopo province. Ephraim Mogale municipality was absorbed into the Schuinsdraai Nature Reserve.
x Bushbuck Ridge municipality was a cross boundary municipality between Limpopo and Mpumalanga and
has now been allocated in full to the Mpumalanga province. (Bushbuck Ridge also absorbed a portion of the
Kruger Park cross boundary District Management Area.)
KwaZulu-Natal and Eastern Cape:
Umzimkulu, formerly in Eastern Cape, and Matatiele, formerly in KwaZulu-Natal were in effect exchanged, with
Umzimkulu now being in KwaZulu-Natal and Matatiele now being in Eastern Cape based on the current provincial
boundaries.
2.2 Local municipal boundary changes, 20012011
In 2001, the Geographical Frame consisted of 262 local municipalities. This total has been reduced to 234 local
municipalities in the 2011 geographical frame. The difference of 28 municipalities is explained as follows:
In total, 25 District Management Areas (DMAs) were absorbed into the existing provinces.
x The City of Tshwane absorbed a further two municipalities (Nokeng Tsa Taemane and Kungwini).
x A new municipality (Kagisano Molopo NW379) was established by merging NW391 (Kagisano) and
NW395 (Molopo).
For municipalities, 107 municipalities decreased in geographical area while 155 municipalities had an increase in
geographical area.
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Map 2.2: Municipal boundary changes since 2001
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Figure 2.1: Percentage distribution of land area by province, 2011
2.3 Comparing Census 2011 with previous CensusesComparison of Census 2011 with previous Censuses (1996 and 2001) required alignment of data for the two
Censuses to 2011 municipal boundaries. This is because the countrys provincial demarcations underwent
changes through a number of changes at provincial and municipal boundaries. The provincial and municipal
changes are outlined below.
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3. FINDINGS
The 2011 Population and Housing Census was comprehensive, with a number of topics: demographics, migration,
general health and functioning, education, parental survival status, employment, fertility, mortality and statistics on
households. The section presents key findings from each of the outlined topics.
3.1 Demographic characteristics
3.1.1 Introduction
A Census is the basic source of demographic information at all levels of geography in a given area at a defined
time. This chapter provides information on size, composition and structure of the population of South Africa from
19962007.
3.1.2 Population size
Figure 3.1 indicates that the population size of South Africa has increased noticeably from 40,5 million in 1996 to
51,7 million in 2011. KwaZulu-Natal, followed by Gauteng had the majority of population in both the two Censuses(1996 and 2001), but was overtaken by Gauteng during C S 2007, leaving KwaZulu-Natal to take second place.
However, there was a noticeable increase in the share of the population in Gauteng from 18,8% in 1996 to 23,7%
in 2011 while the share of the population in KZN remained almost constant (21,1% in 1996 to 19,8% in 2011).
Amongst all the provinces, Northern Cape had the lowest share (2,5% in 1996 and 2,2% in 2011). Eastern Cape on
the other hand showed a marked decline from 15,1% 1996 to 12,7% in 2011.
Figure 3.1: Percentage distribution of population by province, 19962007
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Table 3.1: Population and percentage change by province, Censuses 1996, 2001 and 2011, and CS 2007
Province 1996 2001
1996-2001%
change 2007
2001-2007%
change 2011
2007-2011%
change
WC 3 956 875 4 524 335 14,3 5 278 584 16,67 5 822 734 10,3
EC 6 147 244 6 278 651 2,1 6 527 747 3,97 6 562 053 0,5
NC 1 011 864 991 919 -2,0 1 058 059 6,67 1 145 861 8,3
FS 2 633 504 2 706 775 2,8 2 773 058 2,45 2 745 590 -1,0
KZN 8 572 302 9 584 129 11,8 10 259 229 7,04 10 267 300 0,1
NW 2 936 554 2 984 097 1,6 3 056 083 2,41 3 509 953 14,9
GP 7 624 893 9 388 855 23,1 10 667 505 13,62 12 272 263 15,0
MP 3 124 203 3 365 554 7,7 3 643 507 8,26 4 039 939 10,9
LP 4 576 133 4 995 462 9,2 5 238 285 4,86 5 404 868 3,2
SouthAfrica 40 583 572 44 819 777 10,4 48 502 057 8,,22 51 770 560 6,7
Table 3.1 provides the provincial percentage share of the total population in four periods (19962001, 20012007
and 2007 to 2011). The results show a noticeable decrease of -2% in the percentage share of the total population
of Northern Cape from 1996 to 2001 that increased to 8,3% in 2011. Gauteng, Western Cape and KZN show a
marked decrease of the population share of 15%, 10,3% and 0,1% in 2007 to 2011 respectively.
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Table3.2:Pe
rcentagedistributionofthepop
ulationbypopulationgroupan
dprovince,19962011
Province
BlackAfrican
Coloured
Asian
White
Other
1996
2001
2007
201
1
1996
2001
2007
2011
1996
2001
2007
2011
1996
2001
2007
2011
2011
WC
21,6
26,7
30,1
32,9
56,0
53,9
50,2
48,8
1,1
1,0
1,3
1,0
21,4
18,4
1
8,4
15,7
1,6
EC
86,6
87,2
87,6
86,3
7,7
7,7
7,5
8,3
0,3
0,3
0,3
0,4
5,4
4,9
4,7
4,7
0,3
NC
44,9
46,5
39,8
50,4
43,7
42,9
50,0
40,3
0,2
0,2
0,2
0,7
11,2
10,3
1
0,0
7,1
1,6
FS
84,8
88,0
87,1
87,6
3,0
3,1
3,0
3,1
0,1
0,1
0,2
0,4
12,1
8,8
9,6
8,7
0,3
KZN
82,8
85,2
86,0
86,8
1,4
1,5
1,4
1,4
9,3
8,3
8,1
7,4
6,6
5,0
4,4
4,2
0,3
NW
90,1
90,0
91,2
89,8
1,6
1,8
1,7
2,0
0,4
0,3
0,4
0,6
7,9
7,8
6,7
7,3
0,3
GP
72,3
75,2
75,4
77,4
3,6
3,6
3,7
3,5
2,1
2,3
2,6
2,9
22,0
18,8
1
8,3
15,6
0,7
MP
91,0
93,2
92,0
90,7
0,7
0,7
0,8
0,9
0,4
0,3
0,4
0,7
7,9
5,9
6,8
7,5
0,2
LP
96,9
97,0
97,5
96,7
0,2
0,2
0,2
0,3
0,1
0,2
0,2
0,3
2,8
2,7
2,2
2,6
0,2
SA
77,4
79,0
78,9
79,2
9,0
8,9
9,0
8,9
2,6
2,5
2,6
2,5
11,0
9,6
9,5
8,9
0,5
Table3.2indicatesthatBlackAfricanpopulationgrouphasthehighestproportionofover70%
inallprovinceswiththeexceptionofNorthernCa
peandWesternCape
wherethepe
rcentageswere32,9%
and50,4
%,respectively2011.Ontheotherhand,Colouredpopulationis
thehighestintheNorthernCapeandWesternCape.
However,the
figureshowsadecreasingpatte
rninNorthernCapeof43,7%in
1996to40,3%
in2011and56%
to48,8%
inWesternCape.The
highestpercentageof
IndianorAsia
npopulationisfoundinKwaZulu
-Natal.Thepercentageofthispopulationintheprovincewas9,3%
in1996and7,4%
in2011.West
ernCapeprovinceand
Gautenghad
thehighestpercentagesofthewh
itepopulationgroupat21,4%
and22%in1996whichdeclinedto1
5,7%and15,6%respectively.
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Figure 3.2: Percentage distribution of the population by functional age-groups and sex, Censuses 1996,
2001, 2011, and CS 2007
Figure 3.2 indicates that from 1996 to 2011 the proportion of the population aged 0-14 for both males and females
decreased. The male population in this age group decreased from 35,6% in 1996 to 30,3% in 2011 whilst that of
female population decreased from 33,2% to 28,1%. The proportion of economically active population (15-64)
increased for males from 60,5% in 1996 to 65,6% in 2011. That of females increased from 61,1% in 1996 to 65,4%.
3.2 Population composition
Figure 3.3: Percentage distribution of the population by sex, Censuses 1996, 2001 2011 and CS 2007
Figure 3.3 presents the percentage distribution of the population by sex in the three Censuses and CS 2007.
Overall; the results indicate that the population is predominantly female. On average, the population consists of
48,2% of the male population and 51,7% of the female population
3.3 Sex ratio
Sex ratio is one of the key measures of sex composition. It gives the number of males for every 100 females. If it is
above 100, it shows the predominance of males over females, conversely when it is lower than 100, the reverse is
true. Generally sex ratios at birth are high and decrease gradually as age increases.
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Figure 3.4: Age-specific sex ratios, Censuses 1996, 2001, 2011 and CS 2007
Figure 3.4 indicates that the overall sex ratio increased from 92 in 1996 to 95 in 2011. In general, more males are
born than females hence sex ratios above 100 are expected at younger age groups. The table further indicates an
unexpected pattern of sex ratios of 93 and 91 at ages 2529 and 3034 in 2001 that increased markedly to 101
and 102 in 2007 and remained the same for 2011.
Figure 3.5: Overall sex ratios by province
The results in Figure 3.5 show that among all the provinces, GP and NW had the highest sex ratios of over 100 in
2011. NW had a sex ratio of 98 in 1996 that increased to 103 in 2011. Conversely, Limpopo and Eastern Cape had
sex ratios lower than 90 across the years. Evidence from 1996 and 2001 Censuses showed that the two provinces
(Limpopo and Eastern Cape) were the most affected by outmigration in terms of inter-provincial migration.
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3.4 Median age
Generally median age gives an indication of whether the population is young, old or intermediate. Shryock et al1.
(1976) described a population as being young when it has a median of less than 20 and those with medians of 30
and above as being old. Those with median ages between 20 and 29 are referred to as populations of intermediate
age.
Figure 3.8: Median age by province
Figure 3.8 indicates that the overall median age in South Africa was 22 (1996), 23 (2001), 24 (2007) and 25 (2011)
respectively. This implies that South Africa had intermediate populations in the four periods. Gauteng, followed by
Western Cape had higher but consistent median ages over time, whilst Limpopo and Eastern Cape had lower
median ages of 20 and lower than 20 in 1996 to 2001 that steadily increased to 22 in 2011.
Figure 3.9: Median age by population group
Although South Africas population had the median age of between 22 and 25 in the period 19962011, the results
pertaining to the four main population groups show a different pattern. Figure 3.9 indicates that the white population
had a relatively old population with an increasing median age from 33 to 38 from 1996 and 2011 respectively. On
the other hand, Black African and the Coloured populations structures were predominantly intermediate with themedian ages ranging from 21 to 26.
1Shryock et al. (1976) (1971). The methods and materials of demography. U.S. Bureau of Census, Washington
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3.5 Population structure
Knowledge about the age-sex distribution of a population is valuable information on demographics and socio-
economic concerns. Amongst its importance, it can be used to evaluate, adjust the completeness and accuracy of
Census counts. Figures 3.10 to 3.13 show the pyramids for the three Censuses and the CS 2007 at national level.
Nationally, the figures show that there was a fairly large proportion of females than males in all age groups exceptfor younger age groups where the proportion of males is higher than females. The population in 1996, 2001, and
2007 began to increase from 59 age groups and decreased as age increases. Contrary; in 2011, there was a
marked decrease of males and females aged 59 and 1014. Many factors could have contributed to this
decrease. Further analysis is scheduled to be done to ascertain the key drivers to this occurrence.
Figure 3.10 (1996) Figure 3.11 (2001)
Figure 3.12 (2007) Figure 3.13 (2011)
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3.6 Concluding remarks
The population of South Africa increased from 40,5 million in 1996 to 51,7 million in 2011.
Age-sex distribution indicates a marked decline of both males and females at ages 5-14.
The provincial share of the population indicates an increase of population in Gauteng from 18,8% in 1996 to 23,7%in 2011, and a decrease in KwaZulu-Natal.
Among the population groups, black African population constitutes more than 75% during the four periods.
Provincial sex ratios suggest that Limpopo and Eastern Cape consistently had sex ratios lower than 90 in the
Censuses 1996, 2001, 2011 and CS 2007.
Functional age group pattern show that 014 age group for both males and females decreased whilst those of the
economically active population (15-64) increased over time.
3.7 Migration
3.7.1 Introduction
Migration can be defined as a change in a persons permanent or usual place of residence2. Along with fertility and
mortality, migration is one of the components of population change. Information on previous and usual province of
residence refers to migration between the 2001 and 2011 Censuses. Lifetime migration on the other hand deals
with movements based on where the person was born and where they currently reside. This section provides
information on internal migration as well as immigration. Information regarding emigration is not part of the
analysis.
3.7.2 Patterns of migration between Censuses 2001 and 2011
Table 3.3 is based on the question "Has (name) been living in this place since October 2001? The March 2011
provincial boundaries were used for this analysis. Respondents were asked to report on the month and year they
moved to the place where the enumeration took place and data therefore only reflect the last movement. Although
a person might have moved several times before the last move, it is unfortunately not possible to ask about all
these movements in a Census.
From Table 3.3 the movements of people from a certain province to another, the in-and out-migration and net
migration are shown. Turnover figures obtained as the summation of in- and out-migration, provide an indication of
total movements.
2Hinde, A.Demographic Methods. London: Arnold. 1998.
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Table3.3:Provinceofpreviousresidencebyprovinceofusualresidence
Provinceof
previous
residence
Provinceofusualresidence
Out-
migration
In-
migration
Net-
migration
Turnover
Western
Cape
Eastern
Cape
Northern
Cape
FreeState
KwaZulu-
Natal
North
West
Gauteng
Mpuma-
langa
Limpopo
WesternCape
5158316
40152
10566
5155
9221
5039
50694
4759
3381
128967
432790
303823
561757
EasternCape
170829
6250135
5081
15542
73831
32341
117964
12001
8877
436466
158205
-278261
594671
NorthernCape
17577
4077
1054841
8559
5708
11478
16019
4202
1907
69527
62792
-6735
132319
FreeState
12644
8155
7103
2524282
8881
24090
74387
10859
5283
151402
127101
-24301
278503
KwaZulu-Natal
21857
19178
2437
11481
9812129
8655
184337
28904
4719
281568
250884
-30684
532452
NorthWest
6013
3085
17000
9917
3882
3146255
103550
8495
14066
166008
273177
107169
439185
Gauteng
74915
40161
9446
31455
55620
75260
10416258
61269
54145
402271
1440142
1037871
1842413
Mpumalanga
7256
3390
1932
5032
12511
13091
122578
3723843
25299
191089
243934
52845
435023
Limpopo
7826
2742
1847
5481
4574
26826
283495
39492
5088084
372283
219426
-152857
591709
Outside
SouthAfrica
113873
37265
7380
34479
76656
76397
487118
73953
101749
*Note:Thistable
excludescaseswheretheprovincewasunspecifiedanddonotknow,InformationONLYobtainedfromQuestionnaireA(Hou
seholdQuestionnaire).
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It can be seen from Table 3.3 that Eastern Cape, Northern Cape, Free State, KwaZulu-Natal and Limpopo
experienced a net out-flow of people during the 10-year period (between the 2001 and 2011 Censuses). Western
Cape and Gauteng showed the highest in-flow figures.
Surprisingly, North West and Mpumalanga also showed high net in-flow. Focusing on the North West province, it
seems that the highest migration interaction was with Gauteng and that North West gained less people from
Gauteng than it lost to it (75 260 against 103 550). It is also worth noting that the highest inflow to the North Westcame from outside South Africa (about 28% of all inflow). The turnover data also revealed some interesting figures.
Some provinces with a low net migration showed relative high turnover numbers. KwaZulu-Natal for example has a
net migration loss of 30 684, but the turnover is just over half a million. This is also the case with the Free State and
North West.
3.7.3 Life-time migration patterns
In the previous section we discussed the migration patterns based on the usual residence and previous residence
(ten years before the Census) information. For the analysis in this section the birth province will replace previous
residence to give life-time migration patterns. The results can be found in Table 3.4. Patterns however become
clearer if we calculate net migration and turnover numbers.
Table 3.4 shows that Eastern Cape, Northern Cape, Free State, KwaZulu-Natal and Limpopo all had negative net
migration figures, meaning that more people have migrated out of these provinces than have moved in over time.
Life-time migrants to Gauteng are high as expected but surprisingly high net figures are also found North West and
Mpumalanga. If we focus only on those that were born outside North West, it seems that 20% were born outside
South Africa, and about 13% and 22% in the Free State and Gauteng respectively. Doing the same analysis with
Mpumalanga it is found that 19% were born outside the country and 23% and 21% were born in Gauteng and
Limpopo respectively.
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Table3.4:Provinceofbirthbyprovinceofu
sualresidence
Provinceofbirth
Provinceofusualresidence
Out-
migration
In-
migration
Net-
migration
Turnover
Western
Cape
Eastern
Cape
Northern
Cape
FreeState
KwaZulu-
Natal
North
West
Gauteng
Mpuma-
langa
Limpopo
WesternCape
4018091
104038
26972
19276
32349
14946
183295
16816
19954
417646
1556649
1139003
1974295
EasternCape
887871
5962091
22100
66864
278627
91929
528399
62289
20768
1958847
381467
-1577380
2340314
NorthernCape
84250
23141
951651
27390
57930
46309
90840
26112
6241
362213
163606
-198607
525819
FreeState
46622
23476
20737
2307171
37263
95359
377450
47135
18484
666526
332311
-334215
998837
KwaZulu-Natal
61093
44955
8890
26990
9118900
34298
692568
104753
13006
986553
781153
-205400
1767706
NorthWest
17450
7175
40961
26227
22301
2672138
414183
31619
30180
590096
731474
141378
1321570
Gauteng
165632
82795
17380
70431
122432
162164
6627707
179985
127355
928174
5179842
4251668
6108016
Mpumalanga
23124
14623
3958
12086
43877
41134
505628
3148304
77287
721717
783773
62056
1505490
Limpopo
15236
7044
3263
16115
20752
96387
1277734
165028
4
793131
1601559
474700
-1126859
2076259
Outside
SouthAfrica
255371
74220
19345
66932
165622
148948
1109745
150036
161425
*Note:Thistable
excludescaseswheretheprovincewasu
nspecifiedanddonotknow.InformationO
NLYobtainedfromQuestionnaireA(HouseholdQuestionnaire).
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3.8 Citizenship
In Census 2001 the question pertaining to country of citizenship was asked, however, this question was not
included in this Census. It is therefore only possible to give a table on province of usual residence by citizenship
(see Table 3.5). As expected Gauteng reported the highest percentage of non-citizenship, followed by North West
(3,5%) and Western Cape (3,2%). Eastern Cape and Northern Cape have the lowest percentage of persons
reporting being non-citizens.
Table 3.5: Province of usual residence by citizenship
Province of usualresidence
Citizenship
NumbersSA citizens Non-citizens Unspecified Total
Western Cape 96,0 3,2 0,9 100,0 5 650 462
Eastern Cape 98,4 0,9 0,7 100,0 6 437 586
Northern Cape 98,8 0,9 0,4 100,0 1 125 306
Free State 97,8 1,9 0,4 100,0 2 663 080
KwaZulu-Natal 98,1 1,1 0,8 100,0 10 113 978
North West 95,9 3,5 0,5 100,0 3 439 700
Gauteng 91,9 7,1 1,0 100,0 11 952 392
Mpumalanga 96,8 2,6 0,6 100,0 3 983 570
Limpopo 96,9 2,6 0,5 100,0 5 322 134*Note: This table excludes cases where the province was unspecified and do not know, Information ONLY obtained from Questionnaire A
(Household Questionnaire).
3.9 Education
3.9.1 Introduction
Access to educational opportunities is a human right. This is why Goal 2 of the Millennium Development Goals
(MDGs) aims to achieve universal primary education and ensure that by 2015, children everywhere, boys and girls
alike, will be able to enroll and complete a full course of primary schooling. Quality education encourages
technology shifts and innovation that are necessary to solve present-day challenges. Through education,
individuals are prepared for future engagement in the labour market, which directly affects their quality of life as
well as the economy of the country. Schools are the building blocks for the learning and socialisation.
The South African Schools Act (1996) made schooling compulsory for children aged 7 to 15 years, while the
Education Laws Amendment Act (2002) set the age admission into Grade 1 as the year in which the child turns
seven (Ramaipato, 2009). The age group is widened to include those who are beyond the compulsory school-going
age, but are still attending some institution, as well as those attending tertiary institutions. Hence a continual
analysis of the countrys educational achievements, or otherwise, is therefore of paramount importance for
measuring the impact of education policy and programmes and to track development.
This chapter focuses on school attendance and educational attainment in 1996, 2001, 2007 and 2011. A
comparative overview of educational attainment and attendance by contributory factors such as population group,age group, sex and province are examined.
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3.9.2 Attendance at an educational institution
Figure 3.14: Percentage of persons attending an educational institution amongst person aged 524 years,
Censuses 1996, 2001 and 2011
Figure 3.14 shows comparisons between Censuses 1996, 2001 and 2011. Up to age 15 years there was a general
increase in the percentage of persons attending an educational institution between 1996 and 2011. However, the
Census 2001 data point was slightly lower than the general trend line for 14 and 15 year olds. The
57 year age group has shown the most significant progress in terms of increased enrolment rates between 1996
and 2011. Amongst individuals 16 years and older, enrolment rates tended to fluctuate from data point to data point
and the only trend that tends to manifest itself for the age cohort 18 to 24 years is that the Census 1996 estimates
tend to be significantly higher than all three other data points for all individual ages between 18 and 24 years.
Figure 3.15: The percentage of individuals aged 524 years and currently attending an educational
institution who attend private and public educational institutions, Censuses 2001 and 2011
According to Figure 3.15 the vast majority of students in South Africa attend public educational institutions. Only
5% of those aged 524 years who were attending educational institutions in 2001 attended private institutions as
opposed to 7,3% in 2011.
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Figure 3.16: The percentage of individuals aged 524 years and currently attending an educational
institution who attend private and public educational institutions, by province, Censuses 2001 and 2011
The provincial distribution of private and public educational institution attendance shows a general increase in
private school attendance across all provinces. The highest attendance for private institution in 2011 was for
Gauteng (16,7%), the Western Cape (7,5%) and Free State (6,4%) respectively. All other provinces had private
institution attendance rates of less than 5%.
Figure 3.17: Percentage of persons attending an educational institution amongst those aged 524 years by
population group: 1996, 2001 and 2011
NB: option Other for population group is excluded.
Figure 3.17 compares attendance of educational institutions among persons aged 524 years by population group.Attendance amongst the black African population increased steadily from 70,7% in 1996 to 72,1% in 2001 and
73,9% in 2011. Amongst the Indian/Asian population attendance rates increased from 70,7% in 1996 to 71,8% in
2011. Amongst whites attendance also improved from 70,6% in 1996 to 77,7% in 2011.
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Table 3.6: Percentage of persons aged 524 attending an educational institution by type of institution and
province, 2001 and 2011
Province
Pre-school School CollegeTechnikon/University
Adulteducation
centre Other
2001 2011 2001 2011 2001 2011 2001 2011 2001 2011 2001 2011
Western Cape 5,4 1,5 87,7 90,5 1,9 2,9 4,3 4,5 0,2 0,1 0,4 0,4
Eastern Cape 3,9 0,5 93,7 96,3 0,7 1,3 1,4 1,4 0,1 0,1 0,2 0,3
Northern Cape 4,7 0,8 93,5 96,6 0,9 1,4 0,5 0,6 0,2 0,1 0,2 0,4
Free State 3,8 1,3 92,5 92,8 1,2 2,5 1,9 2,9 0,4 0,2 0,2 0,3
KwaZulu-Natal 3,3 0,5 93,5 94,2 0,9 2,1 1,9 2,5 0,2 0,1 0,2 0,5
North West 4,8 1,0 92,5 94,4 0,9 2,0 1,4 2,1 0,3 0,2 0,2 0,3
Gauteng 5,7 1,7 84,1 85,7 3,8 5,0 5,7 6,8 0,3 0,3 0,4 0,5
Mpumalanga 3,8 0,6 94,3 96,0 0,8 2,0 0,7 1,0 0,1 0,1 0,2 0,3
Limpopo 3,6 0,5 94,8 96,0 0,6 2,0 0,8 1,1 0,1 0,2 0,2 0,3
RSA 4,2 0,9 91,7 93,0 1,4 2,6 2,3 3,0 0,2 0,2 0,2 0,4
Note: Ordinary school and Special school are classified under school. FET and other colleges are combined and classified under college.
Literacy classes and Home based education schooling are classified under other.
The primary reason why there has been a decrease between 2001 and 2011 in children attending pre-school is
that Grade 0 was incorporated into the primary school system, which is also partly reflected in the increased
proportion of individuals who attended school in 2011 compared to 2001.
Figure 3.18: Highest level of education attained amongst persons aged 20 years and older and above,
Censuses 1996, 2001 and 2011
Figure 3.18 shows that the proportion of persons aged 20 years who have no schooling halved from 19,1% in 1996
to 8,6% in 2011. The percentage of persons who have some primary level education decreased from 16,6% in1996 to 12,3% in 2011; whilst the proportion of those who had completed primary level decreased from 7,4% in
1996 to 4,6% in 2011. There was also a considerable increase in the percentage of persons who completed higher
education from 7,1% in 1996 to 11,8% in 2011.
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Figure 3.19: Highest level of education attained amongst persons aged 20 years and older by population
group, 1996, 2001 and 2011
The black African population group has more than doubled the proportion of persons with higher education
between 1996 and 2011. Those with no schooling more than halved during the same time period for the black
African, coloured and Asian/Indian population. The percentage of individuals with no schooling has always been
the lowest amongst the white population group, whilst this group also has the highest proportion of individuals with
a higher education. There has also been a steady increase in the proportion of individuals in this group as well as
amongst Indians/Asians who have attained a higher qualification during the reference period.
Figure 3.19 shows that at the time of Census 2011, 10,5% of black Africans, compared with 4,2% of coloured,
2,9% of Indian/Asian and 0,6% of white, aged 20 years and older had no schooling. A further 36,5% of the White
population attained a level of education higher than Grade 12, compared to 8,3% of black African population, 7,4%
of the coloured population and 21,6% of Indian/Asian persons of Asian origin. The figure also indicates that 35,5%
of the black Africans, 42,0% of coloured persons, 26,1% of Indian/Asian and 21,4% of white persons had at least
some secondary education.
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Table3.7:Highestlevelofeducationamong
stthoseaged20yearsandolde
rbypopulationgroupandsex,
2011
Population
group
Noschooling
Someprimary
Completedprimary
Somesecondary
Grade12/Std10
Higher
Total
Black-African
N
%
N
%
N
%
N
%
N
%
N
%
N
Male
968141
8,7
1546
769
13,9
544120
4,9
4036403
36,4
3122651
28,2
873439
7,9
11
091523
Female
1516351
12,1
1733
245
13,8
604791
4,8
4297729
34,3
3271806
26,1
1095162
8,7
12
519083
Total
2484492
10,5
3280
014
13,9
1148911
4,9
8334131
35,3
6394457
27,1
1968601
8,3
23
610606
Coloured
Male
54682
4,2
177
376
13,6
90574
6,9
555335
42,5
333374
25,5
95491
7,3
1
306831
Female
64334
4,3
210
227
14,0
116729
7,8
617291
41,1
379524
25,3
112820
7,5
1
500924
Total
119015
4,2
387
603
13,8
207303
7,4
1172626
41,8
712898
25,4
208310
7,4
2
807755
Indian/Asian
Male
8987
2,0
21
558
4,8
9807
2,2
118324
26,2
193394
42,8
100016
22,1
452086
Female
17227
3,8
38
595
8,4
15642
3,4
115801
25,3
173901
38,0
96969
21,2
458134
Total
26214
2,9
60
153
6,6
25449
2,8
234124
25,7
367294
40,4
196985
21,6
910220
White
Male
9519
0,6
20
845
1,3
10808
0,7
317114
19,5
647317
39,8
619374
38,1
1
624977
Female
11233
0,6
24
266
1,4
12681
0,7
362288
20,5
737108
41,8
616881
35,0
1
764458
Total
20752
0,6
45
111
1,3
23489
0,7
679402
20,0
1384425
40,8
1236255
36,5
3
389434
Other
Male
10543
8,3
11
539
9,1
5537
4,4
39640
31,4
39159
31,0
19875
15,7
126291
Female
4859
6,8
5
715
8,0
3206
4,5
21654
30,3
21374
29,9
14591
20,4
71399
Total
15402
7,8
17
253
8,7
8743
4,4
61294
31,0
60533
30,6
34465
17,4
197690
Total
Male
1051871
7,2
1778
086
12,2
660846
4,5
5066815
34,7
4335895
29,7
1708194
11,7
14
601707
Female
1614003
9,9
2012
048
12,3
753049
4,6
5414762
33,2
4583713
28,1
1936423
11,9
16
313998
Total
2665875
8,6
3790
134
12,3
1413895
4,6
10481577
33,9
8919608
28,9
3644617
11,8
30
915705
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Table 3.7 indicates that at the time of the 2011 Census; 12,1% of the female black Africans aged 20 years and
older had no schooling as compared to 8,7% of males, 13,8% of females and 13,9% males had some primary
education, whereas 4,8% of females and 4,9% of males had completed primary sch
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